} else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
        for (int i = 0; i < num_dims; ++i) {
          dims[i] = tensor_shape.dim(i).size();
        }
        TFE_OpSetAttrShape(op, attr_name, dims.get(), num_dims, status);
      }
    } break;
    case tensorflow::AttrValue::kFunc: {

                          const int64_t* dims, const int num_dims,
                          ForwardOperation* forward_op_) {
  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",

                                                  int num_dims);

// Get a Matrix TensorHandle with given float values and dimensions
TFE_TensorHandle* TestTensorHandleWithDimsFloat(TFE_Context* ctx, float data[],
                                                int64_t dims[], int num_dims);

// Get a Matrix TensorHandle with given int values and dimensions

    CHECK_EQ(output_shapes->num_items, 1);

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }

    LsarSidArrayX ( jcifs.SID[] sids ) {
        this.num_sids = sids.length;
        this.sids = new lsarpc.LsarSidPtr[sids.length];
        for ( int si = 0; si < sids.length; si++ ) {
            this.sids[ si ] = new lsarpc.LsarSidPtr();
            this.sids[ si ].sid = sids[ si ].unwrap(sid_t.class);
        }
    }


    LsarSidArrayX ( SID[] sids ) {
        this.num_sids = sids.length;

package jcifs.smb1.dcerpc.msrpc;

import jcifs.smb1.smb1.SID;

class LsarSidArrayX extends lsarpc.LsarSidArray {

    LsarSidArrayX(SID[] sids) {
        this.num_sids = sids.length;
        this.sids = new lsarpc.LsarSidPtr[sids.length];
        for (int si = 0; si < sids.length; si++) {
            this.sids[si] = new lsarpc.LsarSidPtr();
            this.sids[si].sid = sids[si];
        }
    }

  // Build an abstract input tensor.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals[] = {0.0f, 0.0f, 0.0f, 0.0f};
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());
  TFE_TensorHandle* t =
      TestMatrixTensorHandleWithInput(eager_ctx, vals, dims, num_dims);

  TF_AbstractTensor* at = TF_CreateAbstractTensorFromEagerTensor(

TF_Tensor* BoolTensor(int32_t v);

// Create a tensor with values of type TF_INT8 provided by `values`.
TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);

// Create a tensor with values of type TF_INT32 provided by `values`.
TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,
                       const int32_t* values);

// Create 1 dimensional tensor with values from `values`

                                                  float data[], int64_t dims[],
                                                  int num_dims) {
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t =
      TFE_AllocateHostTensor(ctx, TF_FLOAT, &dims[0], num_dims, status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);

                                                    TF_Status* status);

// Fills in `dims` with the list of shapes in the attribute `attr_name` of
// `oper` and `num_dims` with the corresponding number of dimensions. On return,
// for every i where `num_dims[i]` > 0, `dims[i]` will be an array of
// `num_dims[i]` elements. A value of -1 for `num_dims[i]` indicates that the